class_docstr: Example agent whose learning curve is a random sigmoid.
  
  The dummy hyperparameters "width" and "height" determine the slope and
  maximum reward value reached.
code: def step(self):
      self.timestep += 1
      v = np.tanh(float(self.timestep) / self.config.get("width", 1))
      v *= self.config.get("height", 1)
  
      # Here we use `episode_reward_mean`, but you can also report other
      # objectives such as loss or accuracy.
      return {"episode_reward_mean": v}
  
file_path: ray/python/ray/tune/utils/mock_trainable.py
imports: import json
  import numpy
  import os
  from ray.tune import Trainable
label_desc: Use image node_id 2 for calling the MyTrainableClass obj's underlying member method code with example usage: obj.step() and returns: dict